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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring successful operation of a cloud solution	20%	- Monitoring and logging - Managing cloud resources
Setting up a cloud solution environment	20%	- Managing billing and identity access - Setting up cloud projects and accounts
Deploying and implementing a cloud solution	20%	- Deploying applications and services - Implementing networking components
Configuring access and security	15%	- Securing data and services - Managing IAM roles and permissions
Planning and configuring a cloud solution	25%	- Configuring compute resources - Configuring storage and database solutions

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Google Associate Cloud Engineer Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
You are using Looker Studio to visualize a table from your data warehouse that is built on top of BigQuery. Data is appended to the data warehouse during the day. At night, the daily summary is recalculated by overwriting the table. You just noticed that the charts in Looker Studio are broken, and you want to analyze the problem. What should you do?

A. Use the BigQuery interface to review the nightly job and look for any errors.
B. In Cloud Logging, create a filter for your Looker Studio report.
C. Use the open source CLI tool, Snapshot Debugger, to find out why the data was not refreshed correctly.
D. Review the Error Reporting page in the Google Cloud console to find any errors.

Answer: A

NEW QUESTION # 161
You are building an application that processes data files uploaded from thousands of suppliers. Your primary goals for the application are data security and the expiration of aged data. You need to design the application to:
*Restrict access so that suppliers can access only their own data.
*Give suppliers write access to data only for 30 minutes.
*Delete data that is over 45 days old.
You have a very short development cycle, and you need to make sure that the application requires minimal maintenance. Which two strategies should you use? (Choose two.)

A. Develop a script that loops through all Cloud Storage buckets and deletes any buckets that are older than 45 days.
B. Set up an SFTP server for your application, and create a separate user for each supplier.
C. Use signed URLs to allow suppliers limited time access to store their objects.
D. Build a Cloud function that triggers a timer of 45 days to delete objects that have expired.
E. Build a lifecycle policy to delete Cloud Storage objects after 45 days.

Answer: C,E

Explanation:
(A) Object Lifecycle Management
Delete
The Delete action deletes an object when the object meets all conditions specified in the lifecycle rule.
Exception: In buckets with Object Versioning enabled, deleting the live version of an object causes it to become a noncurrent version, while deleting a noncurrent version deletes that version permanently.
https://cloud.google.com/storage/docs/lifecycle#delete
(B) Signed URLs
This page provides an overview of signed URLs, which you use to give time-limited resource access to anyone in possession of the URL, regardless of whether they have a Google account
https://cloud.google.com/storage/docs/access-control/signed-urls

NEW QUESTION # 162
You are managing several Google Cloud Platform (GCP) projects and need access to all logs for the past 60 days. You want to be able to explore and quickly analyze the log contents. You want to follow Google- recommended practices to obtain the combined logs for all projects. What should you do?

A. Navigate to Stackdriver Logging and select resource.labels.project_id="*"
B. Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days.
C. Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days.
D. Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days.

Answer: B

Explanation:
* Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right.
Log entries are held in Stackdriver Logging for a limited time known as the retention period which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks.
Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging
* Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right.
While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.
Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
* Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.
Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.
Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks
Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage.
* Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.
Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.
Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks
Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage.
Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.
For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.
Ref: https://cloud.google.com/bigquery/docs/best-practices-storage
Reference: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit- logging

NEW QUESTION # 163
You are deploying a web application using Compute Engine. You created a managed instance group (MIG) to host the application. You want to follow Google-recommended practices to implement a secure and highly available solution. What should you do?

A. Use HTTP(S) load balancing for the MIG and an A record in your DNS public zone with the load balancer's IP address.
B. Use HTTP(S) load balancing for the MIG and a CNAME record in your DNS private zone with the load balancer's IP address.
C. Use SSL proxy load balancing for the MIG and an A record in your DNS private zone with the load balancer's IP address.
D. Use SSL proxy load balancing for the MIG and a CNAME record in your DNS public zone with the load balancer's IP address.

Answer: A

NEW QUESTION # 164
Your company's infrastructure is on-premises, but all machines are running at maximum capacity.
You want to burst to Google Cloud. The workloads on Google Cloud must be able to directly communicate to the workloads on-premises using a private IP range. What should you do?

A. In Google Cloud, configure the VPC for VPC Network Peering.
B. Create bastion hosts both in your on-premises environment and on Google Cloud. Configure both as proxy servers using their public IP addresses.
C. Set up Cloud VPN between the infrastructure on-premises and Google Cloud.
D. In Google Cloud, configure the VPC as a host for Shared VPC.

Answer: C

Explanation:
vpc network peering does not connect to on-prem. Cloud VPN is the correct solution.
https://cloud.google.com/vpn/docs/concepts/overview

NEW QUESTION # 165
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